OpenRaiser/Intern-Atlas huggingface dataset:
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus it is prone to overfitting when using deep CNNs. In this paper, we first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can convey and classify the features effectively. In addition, the proposed data fusion transfer learning strategy is beneficial in boosting the classification performance. Extensive experiments show that the proposed approach beat all of the state-of-art methods on several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center(KSC). Code can be found at: https://github.com/UniLauX/AINet.
A review on outlier/anomaly detection in time series data
Venue: arXiv cs.LG 2020
Year: 2020

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.
Improving the affordability of robustness training for DNNs
Venue: arXiv cs.LG 2020
Year: 2020

Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper we show that the initial phase of adversarial training is redundant and can be replaced with natural training which significantly improves the computational efficiency. We demonstrate that this efficiency gain can be achieved without any loss in accuracy on natural and adversarial test samples. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time by a factor of up to 2.5 with comparable or better model test accuracy and generalization on various strengths of adversarial attacks.
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Venue: arXiv cs.NI 2020
Year: 2020

Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verification and synthesis. Motivated by this, we consider the top-down synthesis of ad-hoc IoT networks for disaster response and search and rescue operations. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks, and, to scale to problem sizes beyond what these frameworks are capable of, develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.
To Share or Not To Share: A Comprehensive Appraisal of Weight-Sharing
Venue: arXiv cs.LG 2020
Year: 2020

Weight-sharing (WS) has recently emerged as a paradigm to accelerate the automated search for efficient neural architectures, a process dubbed Neural Architecture Search (NAS). Although very appealing, this framework is not without drawbacks and several works have started to question its capabilities on small hand-crafted benchmarks. In this paper, we take advantage of the \nasbench dataset to challenge the efficiency of WS on a representative search space. By comparing a SOTA WS approach to a plain random search we show that, despite decent correlations between evaluations using weight-sharing and standalone ones, WS is only rarely significantly helpful to NAS. In particular we highlight the impact of the search space itself on the benefits.
Static and Dynamic Values of Computation in MCTS
Venue: arXiv cs.AI 2020
Year: 2020

Monte-Carlo Tree Search (MCTS) is one of the most-widely used methods for planning, and has powered many recent advances in artificial intelligence. In MCTS, one typically performs computations (i.e., simulations) to collect statistics about the possible future consequences of actions, and then chooses accordingly. Many popular MCTS methods such as UCT and its variants decide which computations to perform by trading-off exploration and exploitation. In this work, we take a more direct approach, and explicitly quantify the value of a computation based on its expected impact on the quality of the action eventually chosen. Our approach goes beyond the "myopic" limitations of existing computation-value-based methods in two senses: (I) we are able to account for the impact of non-immediate (ie, future) computations (II) on non-immediate actions. We show that policies that greedily optimize computation values are optimal under certain assumptions and obtain results that are competitive with the state-of-the-art.
Science through Wikipedia: A novel representation of open knowledge through co-citation networks
Venue: arXiv cs.DL 2020
Year: 2020

This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals.
Hamiltonian Cycle Parameterized by Treedepth in Single Exponential Time and Polynomial Space
Venue: arXiv cs.DS 2020
Year: 2020

For many algorithmic problems on graphs of treewidth $t$, a standard dynamic programming approach gives an algorithm with time and space complexity $2^{\mathcal{O}(t)}\cdot n^{\mathcal{O}(1)}$. It turns out that when one considers the more restrictive parameter treedepth, it is often the case that a variation of this technique can be used to reduce the space complexity to polynomial, while retaining time complexity of the form $2^{\mathcal{O}(d)}\cdot n^{\mathcal{O}(1)}$, where $d$ is the treedepth. This transfer of methodology is, however, far from automatic. For instance, for problems with connectivity constraints, standard dynamic programming techniques give algorithms with time and space complexity $2^{\mathcal{O}(t\log t)}\cdot n^{\mathcal{O}(1)}$ on graphs of treewidth $t$, but it is not clear how to convert them into time-efficient polynomial space algorithms for graphs of low treedepth. Cygan et al. (FOCS'11) introduced the Cut&Count technique and showed that a certain class of problems with connectivity constraints can be solved in time and space complexity $2^{\mathcal{O}(t)}\cdot n^{\mathcal{O}(1)}$. Recently, Hegerfeld and Kratsch (STACS'20) showed that, for some of those problems, the Cut&Count technique can be also applied in the setting of treedepth, and it gives algorithms with running time $2^{\mathcal{O}(d)}\cdot n^{\mathcal{O}(1)}$ and polynomial space usage. However, a number of important problems eluded such a treatment, with the most prominent examples being Hamiltonian Cycle and Longest Path. In this paper we clarify the situation by showing that Hamiltonian Cycle, Hamiltonian Path, Long Cycle, Long Path, and Min Cycle Cover all admit $5^d\cdot n^{\mathcal{O}(1)}$-time and polynomial space algorithms on graphs of treedepth $d$. The algorithms are randomized Monte Carlo with only false negatives.
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages
Venue: arXiv cs.LG 2020
Year: 2020

Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8\% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy.
Reproducibility Report for the Paper: Modeling of Request Cloning in Cloud Server Systems using Processor Sharing
Venue: arXiv cs.PF 2020
Year: 2020

The authors have uploaded their artifact on Zenodo, which ensures a long-term retention of the artifact. The code is suitably documented, and some examples are given. A minimalistic overall description of the engine is provided. The artifact allows to setup the environment quite quickly, and the dependencies are well documented. The process to regenerate data for the figures in the paper completes, and all results are reproducible. This paper can thus receive the Artifacts Available badge and the Artifacts Evaluated-Functional. Given the high quality of the artifact, also the Artifacts Evaluated-Reusable badge can be assigned.
pSPICE: Partial Match Shedding for Complex Event Processing
Venue: arXiv cs.DC 2020
Year: 2020

Complex event processing (CEP) systems continuously process input event streams to detect patterns. Over time, the input event rate might fluctuate and overshoot the system's capabilities. One way to reduce the overload on the system is to use load shedding. In this paper, we propose a load shedding strategy for CEP systems which drops a portion of the CEP operator's internal state (a.k.a. partial matches) to maintain a given latency bound. The crucial question here is how many and which partial matches to drop so that a given latency bound is maintained while minimizing the degradation in the quality of results. In the stream processing domain, different load shedding strategies have been proposed that mainly depend on the importance of individual tuples. However, as CEP systems perform pattern detection, the importance of events is also influenced by other events in the stream. Our load shedding strategy uses Markov chain and Markov reward process to predict the utility/importance of partial matches to determine the ones to be dropped. In addition, we represent the utility in a way that minimizes the overhead of load shedding. Furthermore, we provide algorithms to decide when to start dropping partial matches and how many partial matches to drop. By extensively evaluating our approach on three real-world datasets and several representative queries, we show that the adverse impact of our load shedding strategy on the quality of results is considerably less than the impact of state-of-the-art load shedding strategies.
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Venue: arXiv cs.CV 2020
Year: 2020

Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to segment unknown objects grasped by a robot. Specifically, we exploit motion and temporal cues in RGB video sequences. Using optical flow estimation we first learn to predict segmentation masks of our given manipulator. Then, these annotations are used in combination with motion cues to automatically distinguish between background, manipulator and unknown, grasped object. In contrast to existing systems our approach is fully self-supervised and independent of precise camera calibration, 3D models or potentially imperfect depth data. We perform a thorough comparison with alternative baselines and approaches from literature. The object masks and views are shown to be suitable training data for segmentation networks that generalize to novel environments and also allow for watertight 3D reconstruction.
A Versatile Multi-Robot Monte Carlo Tree Search Planner for On-Line Coverage Path Planning
Venue: arXiv cs.MA 2020
Year: 2020

Mobile robots hold great promise in reducing the need for humans to perform jobs such as vacuuming, seeding,harvesting, painting, search and rescue, and inspection. In practice, these tasks must often be done without an exact map of the area and could be completed more quickly through the use of multiple robots working together. The task of simultaneously covering and mapping an area with multiple robots is known as multi-robot on-line coverage and is a growing area of research. Many multi-robot on-line coverage path planning algorithms have been developed as extensions of well established off-line coverage algorithms. In this work we introduce a novel approach to multi-robot on-line coverage path planning based on a method borrowed from game theory and machine learning- Monte Carlo Tree Search. We implement a Monte Carlo Tree Search planner and compare completion times against a Boustrophedon-based on-line multi-robot planner. The MCTS planner is shown to perform on par with the conventional Boustrophedon algorithm in simulations varying the number of robots and the density of obstacles in the map. The versatility of the MCTS planner is demonstrated by incorporating secondary objectives such as turn minimization while performing the same coverage task. The versatility of the MCTS planner suggests it is well suited to many multi-objective tasks that arise in mobile robotics.
Industry 4.0: contributions of holonic manufacturing control architectures and future challenges
Venue: arXiv cs.MA 2020
Year: 2020

The flexibility claimed by the next generation production systems induces a deep modification of the behaviour and the core itself of the control systems. Over-connectivity and data management abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the cooperation of autonomous and connected entities in the decision-making process. From most relevant articles extracted from existing literature, a list of 10 key enablers for Industry 4.0 is first presented. During the last 20 years, the holonic paradigm has become a major paradigm of Intelligent Manufacturing Systems. After the presentation of the holonic paradigm and holon properties, this article highlights how historical and current holonic control architectures can partly fulfil I4.0 key enablers. The remaining unfulfilled key enablers are then the subject of an extensive discussion on the remaining research perspectives on holonic architectures needed to achieve a complete support of Industry4.0.
Infnote: A Decentralized Information Sharing Platform Based on Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

Internet censorship has been implemented in several countries to prevent citizens from accessing information and to suppress discussion of specific topics. This paper presents Infnote, a platform that helps eliminate the problem of sharing content in these censorship regimes. Infnote is a decentralized information sharing system based on blockchain and peer-to-peer network, aiming to provide an easy-to-use medium for users to share their thoughts, insights and views freely without worrying about data tampering and data loss. Infnote provides a solution that is able to work on any level of Internet censorship. Infnote uses multi-chains architecture to support various independent applications or different functions in an application.
Hidden in Plain Sight: Obfuscated Strings Threatening Your Privacy
Venue: arXiv cs.CR 2020
Year: 2020

String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code. In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing. We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.
Hardware and software co-optimization for the initialization failure of the ReRAM based cross-bar array
Venue: arXiv cs.ET 2020
Year: 2020

Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we examine non-idealities and their impact on the performance. Specifically, we study the impact of failed cells due to the initialization process of the resistive memory based cross-bar array. Unlike the conventional memory array, individual memory elements cannot be rerouted and, thus, may have a critical impact on model accuracy. We categorize the possible failures and propose hardware implementation that minimizes catastrophic failures. Such hardware optimization bounds the possible logical value of the failed cells and gives us opportunities to compensate for the loss of accuracy via off-line training. By introducing the random weight defects during the training, we show that the model becomes more resilient on the device initialization failures, therefore, less prone to degrade the inference performance due to the failed devices. Our study sheds light on the hardware and software co-optimization procedure to cope with potentially catastrophic failures in the cross-bar array.
A polynomial time parallel algorithm for graph isomorphism using a quasipolynomial number of processors
Venue: arXiv cs.DS 2020
Year: 2020

The Graph Isomorphism (GI) problem is a theoretically interesting problem because it has not been proven to be in P nor to be NP-complete. Babai made a breakthrough in 2015 when announcing a quasipolynomial time algorithm for GI problem. Babai's work gives the most theoretically efficient algorithm for GI, as well as a strong evidence favoring the idea that class GI $\ne$ NP and thus P $\ne$ NP. Based on Babai's algorithm, we prove that GI can further be solved by a parallel algorithm that runs in polynomial time using a quasipolynomial number of processors. We achieve that result by identifying the bottlenecks in Babai's algorithms and parallelizing them. In particular, we prove that color refinement can be computed in parallel logarithmic time using a polynomial number of processors, and the $k$-dimensional WL refinement can be computed in parallel polynomial time using a quasipolynomial number of processors. Our work suggests that Graph Isomorphism and GI-complete problems can be computed efficiently in a parallel computer, and provides insights on speeding up parallel GI programs in practice.
Bi-objective Optimization of Biclustering with Binary Data
Venue: arXiv cs.AI 2020
Year: 2020

Clustering consists of partitioning data objects into subsets called clusters according to some similarity criteria. This paper addresses a generalization called quasi-clustering that allows overlapping of clusters, and which we link to biclustering. Biclustering simultaneously groups the objects and features so that a specific group of objects has a special group of features. In recent years, biclustering has received a lot of attention in several practical applications. In this paper we consider a bi-objective optimization of biclustering problem with binary data. First we present an integer programing formulations for the bi-objective optimization biclustering. Next we propose a constructive heuristic based on the set intersection operation and its efficient implementation for solving a series of mono-objective problems used inside the Epsilon-constraint method (obtained by keeping only one objective function and the other objective function is integrated into constraints). Finally, our experimental results show that using CPLEX solver as an exact algorithm for finding an optimal solution drastically increases the computational cost for large instances, while our proposed heuristic provides very good results and significantly reduces the computational expense.